This article explores life-cycle activities for machine learning (ML) within regulated life sciences. It positions and contextualizes the life cycle and management of the machine learning subsystem or components within a wider system life cycle. It also gives general descriptions and guidance illustrated by a case study demonstrating a machine learning application to medical image recognition, or software as a medical device (SaMD).
IT Services: Applying Good IT Practice and Automation Cover: This article focuses on pragmatic quality- and risk-based approaches to IT infrastructure...
Learning Level: Basic/Intermediate - Analytical laboratories are required to demonstrate the suitability of their equipment during audits and will typically follow USP requirements defined in Chapter <1058> on analytical...
Online Live Overview This Virtual course includes the new revised EU GMP Annex 11 , and an update on 21 CFR Part 11 . This two-day fundamental course* introduces participants to regulatory requirements for computerized...

The US FDA Center for Devices and Radiological Health (CDRH) Case for Quality program promotes a risk-based, product quality–focused, and patient-centric approach to computerized systems. This approach encourages critical thinking based on product and process knowledge and quality risk management over prescriptive documentation-driven approaches.